Within the large group of Germanic languages, the dialects of Switzerland belong to the Alemannic group.

while a majority of dialects are High Alemannic (yellow area on map in Figure 1), those spoken in the city of Basel and in the Canton of Valais belong respectively to the Low Alemannic and the Highest Alemannic groups.


The Bible has been translated in several GSW dialects, but the only electronic version available to us were online excerpts in Bernese.

6 this is not translated from High German but from a Greek text, hence the alignment with any of the German Bibles is problematic.


The Workshops on Statistical MT have proposed translation tasks for low-resourced languages to/from English, such as Hindi in 2014 (Bojar et al., 2014), Finnish in 2015, or Latvian in 2017.

these languages are clearly not as low-resourced as Swiss German, and possess at least a normalized version with a unified spelling.


However, training an NMT is not feasible for GSW/DE, as the size of our resources is several orders of magnitude below NMT requirements.

several recent approaches have explored a new strategy: the translation system is trained at the character level (Ling et al., 2015;Costa-jussà and Fonollosa, 2016;Chung et al., 2016;Bradbury et al., 2017;Lee et al., 2017), or at least character-level techniques such as byte-pair encoding are used to translate OOV words (Sennrich et al., 2016).


When pseudo in-domain LM is added, this word got translated to the correct term "இறுதி ஆண்டு" (/iruthi aandu/), where the meaning is 'final year'.

the integration of out-domain data reduced the BLEU scores in both filtered and unfiltered cases in either direction.


Table 3 shows that overall, NMT scores better on accuracy than previous systems.

upon closer inspection, it becomes evident that PBMT handles DNT issues better than NMT.


When scanning the error statistics in Table 5, we can see that also in our data set, NMT makes fewer omission errors than PBMT.

the ratio of omitted words per omission error is much higher in NMT than in PBMT and RBMT.


In standard Word Sense Disambiguation, words are disambiguated based on their textual context.

in a multimodal setting we could also disambiguate words using visual context.


For teams that submitted both multimodal and text-only systems, the role of multimodality is not evident as far as MLT Accuracy is concerned: sometimes multimodal systems perform better and sometimes text-only systems perform better.

human scores show that overall multimodal systems tend to be better than the text-only counterparts.


For English>Japanese, there is strong and significant correlation between |CrossS| and monitoring effort.

there is only a moderate, non-significant correlation for English>Spanish.


mT quality from English to Spanish is good, so the machine will successfully resolve most of the difficult semantic and structural problems.


These resolutions, which will likely be highly salient to the post-editor and which can usually be accepted quickly, will likely reduce the need to expend much monitoring effort on segments that would have required much more effort to translate from scratch.

machine translation solutions for segments where there are few semantic or structural problems to resolve may be much less salient to the post-editor.


Large parallel corpora have been obtained from international bodies or collected from the Web.

they only cover a small subset of the variety of language pairs, domains and genres that are found in language.


This reduces the comparability of our datasets.

the two sides of each dataset still share several dimensions along which they are comparable: • They belong to the same genre distribution, mainly 'encyclopedic article' (see Table 1).


As it can be seen from Table 4 the manual pipeline extracts more parallel sentences (especially for adilet data) and is more accurate than an unsupervised automatic tool.

on average, Bitextor produces cleaner bitexts with lower short/parallel ratios and surprisingly low amount of junk.


There has been an increasing number of natural language processing (NLP) efforts focusing on dialectal Arabic, especially with the increasing amounts of written material on the web.

resources for dialectal Arabic NLP tasks such as part-of-speech (POS) tagging, morphological analysis and disambiguation are still lacking compared to those for Modern Standard Arabic (MSA).


There are lattice shapes which require independent mechanisms for indexing source and tree tokens (or, rather, states in a tree token lattice).

14 addressing all these cases would require a format that could not be directly compatible with the (current version of the) UD format/model.


On the one hand, specialised inter-node connectivity makes BMUs less confusable and more salient, as they receive stronger support through temporal connections than any other node.

less specialised and more blended BMUs are densely and less strongly connected with many others, to meet the input of more words.


morphological breakdowns and glosses), specialized parsers for root-and-pattern morphology and reduplication, and combinators that allow parsing either from the left (for prefixes) and the right (for suffixes).

there are some drawbacks compared to finite-state systems: Efficiency After compilation, a finite-state transducer executes in linear time, while parser combinators result in a recursive descent parser with potentially exponential time complexity.


Tightening up morpho-phonemic rules for better handling of allomorphs and treatment of templatic verbal morphology became the main goals for the second checkpoint.

documentation on numerous templates was incomplete at best.


Figure  3 shows one Oromo phonological rewrite rule implemented with parser-combinators.

10 since these rules are themselves complex parsers, they increase the complexity of the grammar and including many of them can affect the runtime performance of the parser.


akz: murderer, he: pancreas, sh: give birth, and several others).


using n-gram counts (Sornlertlamvanich and Tanaka, 1996), supervised methods (Clouet and Daille, 2014), and monolingual and bilingual corpora (Koehn and Knight, 2003;Macherey et al., 2011) and could be productively employed in extensions of our work.

to several of these other works, the approach and analysis in our paper is simple yet effective in that it only  requires the usually very readily available on-line dictionaries in multiple languages (e.g.


The original drawbacks of the German part of the database were an outdated format and use of obsolete orthographical conventions.

these problems were tackled by Steiner (2016), so that the refurbished database yields a foundation for further exploitation.


In both cases, the sentences were automatically aligned from comparable English Wikipedia and Simple English Wikipedia articles.

the use of EW-SEW dataset for modeling TS has been disputed (Amancio and Specia, 2014;Štajner et al., 2015;Xu et al., 2015) for several reasons: (1) the simplified articles are not necessarily direct simplifications of the original articles; (2) the quality of simplifications is not checked; (3) the dataset does not cover sentence splitting which is one of the most common operations in text simplification.


We noticed that the use of hypothesis H1 reduces the percentage of full matches regardless of language, level pairs, and similarity measure.

it sometimes increases the number of partial matches which model deletion (see Tables 3 and 4).


In the case of aligning Level 0 with Level 4, we also have a higher percentage of full matches on the English dataset than on the Spanish dataset, in addition to a higher percentage of partial matches.

the differences in the percentage of full matches and partial matches between the two languages might not reflect the performances of the system on those languages but rather the nature of simplifications performed on the Newsela arti-   cles in those two languages, i.e.


(2017) detail the specific issues of tokenisation for Picard, as well as the choices made.

to Alsatian and Occitan, the Picard corpus was not pre-annotated.


This suggests that GA is somewhat close to MSA; a similar conclusion reached in (Samih et al., 2017).

it is still far behind the performance on MSA, which is over 96%.


The best performance of Bi-LSTM is 91.2% using CC2W+W representation and meta-types and template features.

the best performance of SVM is 85.96% by setting the clitic feature value to TFIDF and using meta-types features.


Both systems achieved their highest accuracy when trained on the Gulf++ dataset.

bi-LSTM outperforms SVM in most of its settings.


These might have different reasons and can only be resolved using an aforementioned statistical analysis in certain cases.

even in these cases, such an analysis might best be left till after the corpus is annotated.


This shows how encoding all possibilities in the case of gender ambiguity as portmanteau tags can help us to understand the gender system of GML.

neut 85  60  Masc  11  24  neut  34  0  total  130  84   Table 3: Gender of lîf 1 and strît 1 one could suppose that such a detailed annotation as above could be more difficult for the annotator than only using the asterisk and thus could lead to more disagreement between annotators.


As the quantity of annotated language data and the quality of machine learning algorithms have increased over time, statistical part-of-speech (POS) taggers trained over large datasets have become as robust or better than their rule-based counterparts.

for lesser-resourced languages such as Welsh there is simply not enough accurately annotated data to train a statistical POS tagger.


Rule-based POS tagging -whereby pre-defined rules concerning which syntactic categories can be co-located together are used to determine the correct POS tags to assign to word tokens in context -is the traditional alternative to the probabilistic approach.

this introduces an entirely different bottleneck -considerable time and extensive knowledge is required in order to craft and refine the rules in the first place.


The widely-used Brill Tagger attempts to address this bottleneck by automatically acquiring and inferring rules for POS tagging from a running text, and its accuracy has been comparable to that of probabilistic taggers (Brill, 1992).

more pre-annotated data than is typically available for lesser-resourced languages is still required for this approach, and so again it boils down to a decision (costly either way) between crafting enough rules or annotating sufficient training data by hand.


We have had reasonable success in assuming that capitalised words that were unknown prior to running CG were proper nouns -80.56% of these assumptions turned out to be correct, although the accuracy for these tokens using the enriched tagset is rather lower (71.23%) due to the fact that in many of these cases it was simply not possible to discern the gender of the proper noun.

the accuracy of our tagging of ambiguous tokens based on their presence in the coverage dictionary (see Section 3.2.3.)


This approach neither includes any language-specific linguistic information nor requires a large corpus.

they collect all possible words occurring in the same context from the untagged data into a list called contextbased list, thus limiting it from scaling to large monolingual corpus.


The key difference between Word2Vec and FastText is that Word2Vec treats each word in corpus as an atomic entity and generates a vector for each word.

fastText treats each word as composed of ngrams and the vector word is made of the sum of these vectors.


This leads us to the conclusion that there is no difference between the investigated European cultures concerning the directness of the system's output.

there are indeed significant differences on the user's preference of the system's elaborateness.


This leads us to the conclusion that the gender does not influence the user's preference concerning the directness of a system utterance.

the gender seems to influence the preference concerning the elaborateness.


These results support the conclusion drawn from the results depicted in Figure 6 that the gender may influence the user's preference concerning the elaborateness of the system utterances.

there are no significant differences between men and women for English, Russian and Spanish, what leads us to the conclusion that it depends on the culture whether there are gender differences concerning the elaborateness.


The game is suitable for studying deceptive behaviour.

due to its engaging, multi-party nature, it is also suitable for studying multi-party turn-taking phenomena, and therefore provides the possibility to investigate other research directions as well.


This framework can be used to facilitate the decisions of a wizard, which was the case in the Werewolf scenario.

the framework does also provide the grounds necessary to represent a fully autonomous agent instead of the wizard.


This provides strong support that personal information is a vital component of all chat-oriented dialogue, regardless of culture and participant similarity.


Most of these studies dealt with attribution in English.

to the best of our knowledge, there are no empirical studies on annotating attribution in Arabic to generate a gold standard corpus.


The factors mentioned above provide some preliminary evidence that the content of the ADELE corpus is social and casual, and similar to conversational speech.

the tight central tendency for utterances per conversation is not a feature of casual talk, which tends to be open-ended and thus variable in length.


Theoretical studies on the Information Structure-prosody interface argue that the content packaged in terms of theme and rheme correlates with the intonation of the corresponding sentence.

there are few empirical studies that support this argument and even fewer resources that promote reproducibility and scalability of experiments.


The so-called Information Structure-prosody interface stands out as a solid ground for starting to build up such a communicative model in the computational field.

empirical approaches to the Information Structure-prosody interface are scarce, studies on a corpus of more than two speakers are uncommon and the availability of corpora is, so to say, exceptional.


In excerpt 1, the SC used a similar distal demonstrative aaiu (that sort of), which is generally used as an adjective to strongly project the immediate occurrence of the noun it modifies, as mentioned in 5.1.

are is a demonstrative pronoun, so it can be produced without mentioning the object it refers to when spatial-deictically used.


(2014) constructed a Japanese corpus with discourse annotations through crowdsourcing.

they did not evaluate the quality of the annotation.


Let us consider the following examples: In example (4), the Old Annotation disagrees with the Expert Annotation, but the New Annotation agrees with the Expert Annotation presumably because this pair failed the language tests (we can insert "in order to" 2 between (ii) and (iii)).

the New Annotation disagrees with the Expert Annotation in example (5).


As in Persian there are two pronouns for secondperson: single and plural, the type is classified into four groups.

for direct referent, there are six major groups: identity, inferred, quantifier, cross-speech, event and person/number suffix on verb.


Indeed, some annotators tend to consider more spans of text as argument components than others.

there is a high agreement on spans identified as argumentative by annotators that consider less spans of text as argumentative.


Looking at the confusion matrices of annotations of pairs of annotators, in Figure 2, we find that there are important disagreements between all of the categories.

the category of major claim seems to be the most conflictive: in one of the pairs, annotators did not have any overlap, in the other, they had more proportion of disagreement than of agreement.


In general, there is some confusion between premises interpreted as facts or as case-law, and also between premises considered case-law or law principles.

these confusions can be easily addressed by a formal delimitation of case-law using shallow textual cues, also refining annotation guidelines.


In addition, it has to deal with noisy user input containing spelling and grammar errors.

most approaches to SAS consider automatic scoring as a classification task, relying on supervised machine learning (mL) techniques which require manually labeledtraining data Basu et al.


As the total vocabulary size continues to increase, the vocabulary size index also increases.

during the beginning of children's vocabulary production, the vocabulary commonality index once falls and then goes up again.


These findings may play important roles in further studies of child language development.

the results of this study are limited to 2,688 words.


The ITA data are compatible with standard Italian as testified by a Google search: nello stesso tempo has 1,5 million occurrences while allo stesso tempo appears 13,3 million times.

the same search on the CORIS corpus (Rossini Favretti 2000) finds 1771 occurrences for nello stesso tempo and just 1908 for allo stesso tempo, so it is necessary to be careful in drawing conclusions from rough data.


approaches with a stronger computational side are also envisaged.


Revita lies at the intersection of two established areas of research: intelligent tutoring systems (ITS) and computer-assisted language learning (CALL)the project seeks intelligent solutions for language learning.

revita has the potential 1 The system is online at revita.cs.helsinki.fi for enriching the language teaching process as well, because the platform can be used for collecting, mining and analyzing educational data.


This ratio is a key factor that has suppressed wider emergence of CALL/ITS beyond the beginner levels.

offering a fixed, limited set of exercises is in conflict with the principles of adaptability of the learning process to the profile of the particular user.


This is in-line with linguistic expectations given Inuktitut's regular agglutinative morphology; there are few combinations of bigrams delimited by white space.

compare Indonesian, which has a higher key parameter (the number of connections a node has).


Regarding small world characteristics, it is not difficult to imagine how their char-acteristic properties, including efficient information transfer and properties of regional specialization, could account for universal properties like fast retrieval from the mental lexicon.

more substantive work is needed to show for example that small world properties constrain memory models to facilitate retrieval, e.g.


Besides this, the multi-layer architecture makes possible to add/remove layers when needed, which makes the system more flexible.

standoff annotations make possible to store the different annotations apart from the original text.


Besides this, the system is perfectly integrated in the TEITOK environment: it allows for complex queries at the token level using all the information stored in the corpus through CQP; it makes possible a visual representation of the learner text corrected at three different levels (orthographic, grammatical and lexical).

taking into account what we discussed in section 1, it is clear that this system presents some problems for error annotation: it only works at the token level 2 ; it offers a limited categorization and description of errors types; and it is limited to three linguistic areas, while some errors go beyond those areas.


For the moment, our results indicate that the token-based representation may account for most of the errors found.

these results may be biased by the fact that the annotator has tried to adjust the annotation to the tokenbased representation and we think that a deeper analysis is necessary to draw precise conclusions.


The SGATe (SLA in Grammatically Annotated Texts) resource comprises the entire EF-Cambridge Open Language Database (EFCAMDAT)  annotated with 107 pedagogically relevant grammatical structures.

for diving deeper into the grammatical structures, we did not use the whole corpus, as we explain in this section.


We could have subdivided the North-Occitan subdomain (and the Croissant) into Limousin, Auvergnat and Vivaro-Alpine.

the more we multiply borders, the more we create problems: speakers from Velay, for example, are difficult to classify.


The applied protocol (a translation) can also have an influence, prompting us to take a critical look at the productions so elicited.

depending on the speakers, the translation can favour both calques and a search for maximum deviation from French.


Our analysis in Section 2 2 indicates that this could be achieved by issuing more search engine queries.

scannell (2007) argues that web crawling using seed URLs returned by the queries is important for building corpora for low-resource languages.


The results obtained by training on OLCA67 and then applying the model to OLCA68 (and the other way round) could indicate that the use of the same spelling system plays a positive role.

it should be mentioned that OLCA67 and OLCA68 also roughly correspond to the same vocabulary set, only for two different dialectal areas,  and this could account for the results obtained.


Prior work on this task has relied on the use of MPs' division votes as sentiment polarity labels, under the assumption that these votes represent the speakers' opinions to-wards the subjects under discussion: votes for 'Aye' (that the motion be approved) or 'No' (that it be negated) are presumed to indicate positive and negative sentiment, respectively.

as MP voting is to a large extent constrained by party affiliations, with members often under pressure to follow the party whip regardless of their personal opinion (Searing, 1994;Norton, 1997), we perform sentiment analysis experiments on the Hansard Debates with Sentiment Tags (HanDeSeT) corpus, which features manually annotated sentiment labels in addition to those extracted from division votes .


For example, for motions that commend the Government, speeches which support the motion are likely to incorporate positive language, while those that oppose the motion will tend to include typically negative language.

for motions that oppose Government policy, speeches favourable to the motion are themselves also likely to use typically negative language towards the Government, and unfavourable speeches will conversely use positive language, as in Example 1.


They are wrong in principle and in this case.

we are realistic and we know that the Government have a majority.


2 In this step we perform sentence segmentation, tokenization, lemmatization, morphological analysis, part-of-speech tagging and dependency parsing, following the Universal Dependencies scheme (Nivre et al., 2016).

the pre-processing set-up is slightly complicated by the fact that the Norwegian language has two official written standards -Bokmål (the main variety) and Nynorsk -both of which are represented in the review corpus.


This has often been based on single-domain datasets, and examples include (for English unless otherwise noted) movie reviews collected from aggregator sites like IMDb.com (Pang and Lee, 2004;Maas et al., 2011) and RottenTomatoes.com (Pang and Lee, 2005;Socher et al., 2013), hotel reviews from TripAdvisor (Wang et al., 2010), book reviews (in Arabic) (Aly and Atiya, 2013), app reviews compiled from Apple App store and Google Play (Guzman and Maalej, 2014), and reviews of restaurants and other businesses in the Yelp open dataset.

5 the unbalanced nature of these datasets (single domains) can impose inherent limitations on the ability of 5 https://www.yelp.com/dataset models to generalize.


As we had only referenced if a sentence is positive or negative without taking into account the intensity of how it is expressed, we have not taken into consideration if a sentence has multiple occurrences of the same polarity on different words and only considered as it is positive or negative.

for the abstractive summaries we used Spanish Sentistrength (López et al., 2012) for the analysis.


This can be attributed to a high correlation of a classifier with words and phrases that are specific for the positive and negative utterances of the given domain.

language expresses some lexical means of conveying sentiment polarity in a way that is shared across different domains.


synsets that include both positive and negative LUs.

they comprise only 3.8% of all marked synsets, i.e.


between strong vs weak or weak vs neutral.

to SentiWordNet the manual annotation in plWordNet is done only on the level of LUs (Zaśko-Zielińska et al., 2015) and synsets are not manually assigned sentiment polarity values.


An observed precision and recall for positive and negative reviews is slightly different, especially when we compare a model using randomly generated lexicon (RAND) with the models using lexicons constructed in a controlled way (BASE, CPP-N).

the difference between rule-based propagation and CPP is small which may suggest that hybrid methods combining neural approaches with language resources are still imperfect for this  Table 6: Precision (P), recall (R) and F-score (F) for specific polarity classes, in the task of sentence-level sentiment recognition with LR-LSTM.


all these studies have been based on a very small number of sign languages, they only focused on one aspect of iconicity (the choice between the object vs. handling handshapes), and only in one semantic field (namely, instruments).


Another approach is represetned by the ASL-Lex database (Caselli et al., 2016) which contains approximately 1000 ASL signs annotated (among other features) for iconicity ratings.

the ratings only reflect a degree of iconicity (on a 7 point scale) for the whole signs, and does not discuss iconic features.


(2013) who annotated more than 700 ASL signs for iconicity of the three major parameters: hanshape, location, and movement.

these parameters were only annotated as being iconic or non-iconic, without further analysis of iconicity.


In addition, the procedure of data collection lead to the fact that lexical signs and multi-sign descriptions of non-lexicalized concepts are not systematically distinguished.

since we focused only on the most basic concrete concepts, we consider the data to be good enough for our purposes.


Like the original Swadesh list, Woodward's list contains 100 items that are meant to identify basic/universal concepts which are supposed to reveal the degree to which pairs of SLs are related.

this method has not been systematically tested or applied to SLs.


Such a comparison produces results like those shown in Table 1  These approaches are based on pairwise comparisons of SLs and they show that the lexicostatistics method can be successfully applied to languages in the visual modality (but see Section 4 for commentary on some of the problems of this method of comparison).

previous research has not attempted a systematic comparison of a large sample of SLs.


Previous studies compared signs by looking at the global similarities of the four main classes of phonemes (Handshape, Location, Movement and Orientation).

none of them has been explicit on how similarity is measured.


In this study, we decided to consider all features and not to apply any weight correction.

we show the effect of collapsing some feature values for handshape and place of articulation.


One involves a picture sequence corresponding to the chronological sequence of the events depicted, about RMS Titanic.

to avoid productions too strictly focused on signing each picture in turn, we have included informational pictures (size of ship, number of life boats, etc.)


A major advantage of deep sequence generation models such as Recurrent Neural Networks (RNNs) is that they do not require the training data to be pre-segmented (Graves, 2013).

for eventual use in baseline networks or automatic sentence segmentation models, additional time annotations for separation of all lexical items in the corpus were determined.


We evaluated the corpus usability for the learning of JSL sentence structure with a straightforward modification of the sequence to sequence model (Seq2Seq) for English-French translation (Sutskever et al., 2014).

the determination of generated sequence quality is a difficult task that is commonly performed by rigging the generated sequences on a virtual character, and by subsequently assessing their naturalness and understandability in user studies.


Here, it should be noted that deep networks are commonly trained on much larger data sets.

since SL data collections cannot be acquired as easily as text or image data, the number of available training data can already be considered numerous for the given data content.


That particularly concerns Head and Neck Cancers (HNC), because their treatment can be mutilating and disabling.

the usual tools for assessing QoL are not relevant for measuring the impact of the treatment on the main functions involved by the sequelae.


One reason for this is that they combine experience with multiple sources of information.

there are some critical drawbacks of manual segmentation which make it impractical for large speech corpora.


The results indicate that the proportion of adverbs in the missing translations is 26.8% greater than the average proportion of all missing translations (33.1%).

the proportion of nouns is 7.9% less than the average, which infers that nouns tend not to be omitted.


Thus, names and numbers do not trigger problems for interpreters.

adverbs, which play a modifying role in sentences, similar to adjectives, show a 27.9% greater proportion of missing translations than adjectives.


We know that for multi-speaker acted emotions, classification rates usually reach high performance (for example with corpora such as EMO-DB).

with multi-speaker spontaneous speech, the classification rates are much lower, thus reflecting the difficulty to discriminate emotions in such a context (Schuller et al., 2009b).


For example, understanding image descriptions is crucial for interpreting the requests quoted above, as all of them contain image descriptions (my wedding dress; my dog's eyes; the people in the background; my ex).

to our knowledge, no work has yet attempted to tackle the specific task of automated image editing through natural language.


cases that contain an event introduced through a nominal expression.

she excludes those grammatical elements that introduce relative clauses or pronouns (as who in "I don't know who you are.")


id="4" source = "wiki 39733 Papa Pio XII" text: .. id="5" source = "wiki 1041014 Divisione Nazionale" text: .. Level-of-detail:Arg2-as-detail example id="1" source = "Adige 413952" text: .. sidered other 357 documents of the newspaper "L'Adige" (same source of CIB); we will refer to this source as Adige.

we search for additional examples in documents from Wikipedia 8 .


A widely adopted approach involves adopting a threshold for the number of overlapping n-grams randomly sampled be-tween each pair of documents (Broder, 1997).

the results depend on the accuracy and coverage of the sampling procedure in each of the documents, so that larger samples are more likely to produce more reliable indication of redundancy.


A sufficient number of the corpora in several languages, particularly in English, is freely available.

to the best of our knowledge, the Czech one is missing.


There have been attempts to compile corpora more represantative of everyday language by utilizing different sources, especially movie subtitles (Lison and Tiedemann 2016).

the availability of such sources is limited.


In order to accomplish this, we select "typical sentences", defined as sentences with a common syntactic structure (represented as a sequence of POS-tags).

to simplified or controlled languages (like Simplified English (Ogden 1932) or Kontrolliertes Deutsch (Lehrndorfer 1996) (controlled German) there is no set of handwritten rules for syntax and vocabulary.


The biomedical domain offers many linguistic resources for Natural Language Processing, including terminologies and corpora.

most of these resources are prominently available for English and the access to terminological resources in languages other than English may not be so simple.


The presentation of a new Xhosa lexicographical resource for a multilingual federated environment is an example for the transformation of isolated and unpublished dictionary data to the digital age.

the data set used to develop the BantuLM ontology is only a snapshot of a resource in development.


Discontinuous DMs are described as having two orthographic segments.

dMs are described as composed of a single token or as a multiword unit (phrasal).


Some schemas are linked to related frames defined in the FrameNet project (Baker et al., 1998;Ruppenhofer et al., 2006).

in practice, these links are often still missing.


The MetaNet repository readily provides a substantial number of such metaphors for a non-trivial number of schemas.


The paper reported high level of agreement of LCM tags between English verbs and their Polish translations, as Kappa scores ranged from 0.83 to 0.87 depending on person (experiment involved two linguists).

translating verbs from the General Inquirer dictionary into Polish and copying their LCM labels, was not a satisfactory method to obtain a complete LCM dictionary for Polish due to poor coverage.


There are shallow similarities between a WordNet and a thesaurus, which different sets of terms are grouped based on a meaning-similarity criteria.

the labels of the Wordnet are defined by the semantic relationship between the words or entries, while the clusters of words in a synonym dictionary may not follow any distinctive pattern of explicit meaning similarity (Miller, 1995).


In the second example, the synsets do not match so the classification was labeled as incorrect.

in the synset result (spa-30-05598147n) the word "nose" shows up, which is a part of one's face so it's related to what we were originally looking for (chin).


In general, we expected to have good precision for all types of dependencies, since each candidate is matched against the collocations represented in the ontology and the ontology is based on the FLN, which is manually constructed.

we had false positives due to parsing errors.


Amongst those, the Persian language is spoken by more than 110 million speakers world-wide and has more than 570K articles on Wikipedia.

it has been rarely studied for NER (Khormuji and Bazrafkan, 2014) or even just text processing (Shamsfard, 2011).


Similarly, terms like "Kreis" ("county" ) in "[Kreis Tuttlingen]" are included to distinguish the county from the city.

terms like "Ecke" ("corner" ) or "Kreuzung" ("intersection" ) are not included in the extent of Location-Street entities, because they are not an integral part of the location's name.


a CompanyProvidesProduct relation from a news text like "Sensata Technologies' products include speed sensors, motor protectors, and magnetic-hydraulic circuit breakers" , where the product argument refers to a non-consumer product or product class entity such as "speed sensors" or "magnetic-hydraulic circuit breakers" .

when it comes to such specific domains, developing named entity recognition algorithms is severely hampered by the lack of publicly available training data and the difficulty of accessing existing dictionary-type resources, such as product catalogs.


An LG is applied to these files without any markup and the NEs identified by it are annotated.

the segmented files are tokenized using the OpenNLP 2 library.


As with the setting of supervised learning, building NER systems needs a massive amount of labeled training data which are often annotated by humans.

for most languages, large-scale labeled datasets are only readily available in some domains, for example the news domain.


Embeddings are especially helpful when there is little training data, since they can be trained on a large amount of unlabeled data.

training embeddings for Chinese is not straightforward: Chinese is not word segmented, so embeddings for each word cannot be trained on a raw corpus.


Such resources are truly valuable only if they are enriched with different layers of linguistic annotation ranging from morphology and syntax to semantics.

there are many researchers who (want to) use corpora in their everyday work and look for various occurrences of specific words, forms or patterns, syntactic functions, etc.


In case of Sandhi, several examples in the Sand-hiKosh corpus contain more than two words to be joined.

all the three tools have provision for joining only two words at a time.


In fact, two levels of numbering (i.e., section and subsection identifiers) cover all complex sentences in CLTT.

this strategy could be easily extended to other numbering levels.


It was later modified to produce also the enhanced UD graphs from the basic trees, consulting the original LVTB data as well.

this leads to some inaccuracies, and we plan to rewrite the transformation so that the enhanced graph is built first as it closer follows the original hybrid representation, and then it is reduced to the basic dependencies.


So far we have conducted only limited preliminary experiments on the AMR annotation based on the underlying UD,   PropBank and FrameNet layers, as well as the auxiliary named entity and coreference layers.

it seems feasible to systematically generate draft AMR annotations for manual post-editing, thus, boosting the productivity and acquiring more consistent AMRs.


For example, the Chinese Penn Treebank has clear annotations about the locations of traces in relative clauses but the fillers are 'WHNP' empty categories.

the head noun of a relative clause can always be reliably and accurately located given the tree structure.


For example, the Stanford dependencies of (2) will contain 'rcmod(news, do-not-have)' and the Stanford dependencies of (3) will contain 'rcmod(happiness, need)'.

this dependency label does not provide any information about whether the head noun is the first or second argument of the verb.


By these principles, we mapped the rcmod dependencies into subject or object relative clause dependencies in our annotations.

it is more difficult to recover nonlocal dependencies for topic relative clauses.


Shamao is annotated as a verb in (10) in Treebank annotations and shamao de forms a relative clause modifying xiangsheng 'man'.

the gcg-l parser parses the word as a noun and the structure of the noun phrase is 'noun + de + noun', which is also a very common structure for noun phrases in Mandarin Chinese.


Ideally, this is a word that corresponds to the historical original both in meaning and etymology.

depending on the strategy employed by the respective module, this may also be a translation, if a corresponding word cannot be found or if its meaning has changed.


A possible explanation for this is that English word order is more rigid and shows a higher number of right-headed dependencies (including explicit subjects).

italian and Spanish, characterized by a more flexible word order, show a higher variability at the level of dependency direction.


It is accessible both online and offline.

this browser is compatible with the database created by the polish editor, which makes it not independent from this editor.


The wordnets it resorts to have permissive licences for derivatives and redistribution and searching through the browser shows results in all their languages.

the source code of the browser itself is not available to be reused, and it is a browser that in any case offers no options to peruse wordnets on the basis of, direct or transitive, semantic relations.


By allowing to look also for the translations of the lemma searched for in the wordnet of interest, the browser presented in Section 4. permits the perusing of any wordnet, on which it is based, in a multilingual setting.

this still offers a quite limited compliance with a truly multilingual browser.


For example, the knowledge encoded in the WordNet relations part(elementary particle 1 n ,atom 1 n ), member(national 1 n ,country 3 n ) and substance(cartilage 1 n ,cartilaginous structure 3 n ) can also be inferred from SUMO.

according to our interpretation of the meronymy relations of WordNet, the knowledge in the relations part(cell 2 n ,cell nucleus 1 n ) and substance(grape 1 n ,wine 1 n ) is incompatible with SUMO.


According to our interpretation of the semantics of substance and the mapping information, we have to use the third QP and the SUMO predicate material r in order to translate the knowledge in substance(grape 1 n ,wine 1 n ) in terms of Adimen-SUMO.

fruitOrVegetable c is defined to be subclass of CorpuscularObject c in SUMO.


Primitives and Basic Concepts Conventional sense representation have used semantic primitives to define and achive canonical representation for concepts (Wierzbicka, 1972), such as Conceptual Dependency representation (Schank, 1975) and HowNet.

using primitives only to define concepts causes information degrading as it is almost impossible to understand a definition of a complex concept merely with primitives.


If orange 橙色 plays the role of object such as in (5), the sense definition should be applied in the composition process.

in (6), orange 橙色 plays the role of modifier so operational expression should be applied.


For example, {老師|teacher} in E-HowNet is a subcategory of { 專 業 人 士 |professional} therefore a hyponym of {human|人}.

'teacher', also denotes a kind of occupation and should be regarded as an 'occupation value' as well.


Therefore, CLIR is a suitable application for such a translation model.

a major obstacle to this approach is the lack of parallel corpora for model training.


It is possible to use a Web crawler to explore the candidate sites completely.

we can take advantage of the search engines again to accelerate the process.


Comparing HTML structures seems to be a sound way to evaluate candidate pairs since parallel pairs usually have similar HTML structures.

we also noticed that parallel texts may have quite different HTML structures.


For example, AT&T, the long distance company, provides their users the following options: "Please say information for information on placing a call, credit for requesting credit, or operator to speak to an operator."

given the improved speech recognition technology, and the research done in natural language dialogue over the last decade, there exists tremendous potential in enhancing these customer service centers by allowing users to conduct a more natural human-like dialogue with an automated system to provide a customer-friendly system.


Due to the large size of the database, we did not attempt to clean the data.

we did build several data structures based on the database which were used by the system.


The parser is domain-driven in the sense that it uses domain-dependent information produced by the lexicon to look for information, in a user utterance, that is useful in the current domain.

it does not attempt to understand fully each user utterance.


Horiguchi outlined how "spoken language pragmatic information" can be translated (Horiguchi, 1997).

she did not apply this idea to a dialogue translation system.


1 In this paper, we use the term syntactic dependency (tree) structure as defined in the Meaning-Text Theory (MTT;Mel'cuk, 1988).

we extrapolate from this theory when we use the term conceptual dependency (tree) structure, which has no equivalent in MTT (and is unrelated to Shank's CD structures proposed in the 1970s).


The PSyntSs may not be valid directly for realization or transfer since they may contain unsupported features or dependency relations.

the PSyntSs are represented in a way to allow the framework to convert them into valid DSyntS via lexicostructural processing.


This is unusual in the field of text processing which has generally dealt with well-punctuated text: some of the most commonly used texts in NLP are machine readable versions of highly edited documents such as newspaper articles or novels.

there are many types of text which are not so-edited and the example which we concentrate on in this paper is the output from ASR systems.


Figure 1: Example text shown in standard and ASR format ation which is not available in ASR output is sentence boundary information.

knowledge of sentence boundaries is required by many NLP technologies.


Both precision and recall are quite promising under these conditions.

this text is different from ASR text in one important way: the text is mixed case.


Reynar and Ratnaparkhi (1997) (Section 2) argued that a context of one word either side is sufficient for the punctuation disambiguation problem.

the results of our system suggest that this may be insufficient for the sentence boundary detection problem even assuming reliable part of speech tags (cf note 5).


This feature may make LSA a useful tool in the detection of a previous question that establishes a presupposed entity in a later question.

questionnaires differ from connected discourse, such as coherent stories, in aspects that make the present problem rather more difficult.


AbsSemRep I-el ~~ .................................... SemRep --(~------ Figure 3: A RAGS view of the CGS system In many NLG systems, (nominal) referring expression generation is an operation that is invoked at a relatively late stage, after the structure of individual sentences is fairly well specified (at least semantically).

referring expression generation needs to go right back to the original world model/knowledge base to select appropriate semantic content to realise a particular conceptual item as an NP (whereas all other content has been determined much earlier).


The identification and normalisation process described in the previous two sections are common to deceptive cognates, technical terms and numerical expressions altogether.

the comparison of the resulting normalised forms as well as the processing they should further undergo is of a rather case specific nature.


In addition, Table 5 shows that the English coreference results in better recall than Romanian coreference.

the recall shows a decrease for both languages for SNIZZLE because imprecise coreference links are deleted.


Predictive Annotation works best for Where, When, What, Which and How+adjective questions than for How+verb and Why questions, since the latter are typically not answered by phrases.

we observed that "by" + the present participle would usually indicate the description of a procedure, so we instantiate a METHODS QA-Token for such occurrences.


Our proposal for three levels of IE is modelled after the MUC standards using MUC-style representation.

we have modified the MUC IE task definitions in order to make them more useful and more practical.


In TREC-8 QA, this is not a problem since every question is guaranteed to have at least one answer in the given document pool.

in the real world scenario such as a QA portal, it is conceived that the IE results based on the processing of the documents should be complemented by other knowledge sources such as e-copy of yellow pages or other manually maintained and updated data bases.


If we were dealing with data that contains case information, we would also include fields representing the existence/non-existence of initial upper case for the five words.

since our current data does not include case information we do not include these features.


With the baseline case we achieve 70.4% precision but with 0% recall.

the decision tree approach obtains 77.1% precision and 73.8% recall.


If we take the baseline approach and assume that all unknown words are names, then we would achieve a precision of 70.4%.

using the decision tree approach, we obtain 86.5% precision and 92.9% recall.


For example, the edit distance feature in the misspelling identifier assumes that words consist of alphabetic characters which have undergone substitution/addition/deletion.

this feature will be less useful in a language such as Japanese or Chinese which use ideographic characters.


Naturally, these are not appropriate comparisons for the work reported here.

as is evident from the discussion above, previous spelling research does provide an important role in suggesting productive features to include in the decision tree.


Capitalization is his sole means of identifying names.

capitalization information is not available in closed captions.


More importantly, we investigate the relative role of each statistical and linguistic knowledge source in the proposed IR/NLP question-answering system.

to previous results, we find that statistical knowledge of word co-occurrences as computed by vector space models of IR can be used to quickly and accurately locate relevant documents in the restricted QA task.


In the cases we have investigated, this strategy works often enough to be useful for the surface object.

due to predicate coordination as well as relativization, such a strategy often fails to identify correctly the surface subject of bind (binds or bound) when more than one binding term precedes the verb.


Basically, the segmenter is based on frequency of individual nouns extracted from corpus.

the problem is that it is difficult to distinguish proper noun and common noun since there is no clue like capital letters in Korean.


Assume that 'sl' is a frequently appearing word in texts whereas 's2s3s4' is a rarely occurring sequence of syllables as a word.

'sis2' and 's3s4' occurs frequently but although they don't occur as frequently as 'sl'.


both hag-gyo and saeng-hwal are frequently occurring syllables, and actually they are all words.


It is important to note here that we would probably have obtained this improvement in recognition accuracy even with a manual revision of the grammars.

the main advantage in using our tool is the tremendous simplification of the whole process of revision for a grammar developer who now selects counter-examples with an interactive tool instead of manually revising the grammars.


We have successfully used the tool to constrain overgeneralizing grammars of speech understanding systems and obtained 20-30% higher recognition accuracy.

we believe the primary benefit of using our tool is the tremendously reduced effort for the grammar developer.


we propose a framework of language independent morphological analysis system.


Disambiguation i s already language independent, since it does not process strings directly and therefore will not be taken up.

tokenization and dictionary look-up are language dependent and shall be explained more in this paper.


If a hyphenated segment such as "data-base," "F-16," or "MS-DOS" exists in the dictionary, it should be an independent lexeme.

if a hyphenated segment such as "55-years-old" does not exist in the dictionary, hyphens should be treated as independent tokens (Fox, 1992).


Then we pass it to the analyzer for non-segmented languages.

the analyzer may return the result as "They / 've / gone / to / school / to / get / her / ."


The set of the delimiters acting as boundaries: These act as boundaries of MFs.

these can not be independent MFs (can not start nor end a lexeme).


These problems are discussed by several authors including Mills (Mills, 1998) Webster & Kit (Webster and Kit, 1992).

their proposed solutions are not language independent.


Using case-sensitive rules is straightforward since generally only nouns (and proper names) are written in standard German with a capitalized initial letter (e.g., "das Unternehmen" -the enterprise vs. "wir unternehmen" -we undertake).

for disambiguation of word forms appearing at the beginning of the sentence local contextual filtering rules are applied.


company names) may appear in the text either with or without a designator, we use a dynamic lexicon to store recognized named entities without their designators (e.g., "Braun AG" vs. "Braun") in order to identify subsequent occurrences correctly.

a named entity, consisting solely of one word, may be also a valid word form (e.g., "Braun" -brown).


This means that although the exact attachment point of each individual PP is not known it is guaranteed that a PP can only be attached to phrases which are dominated by the main verb of the sentence (which is the root node of the clause's tree).

the exact point of attachment is a matter of domain-specific knowledge and hence should be defined as part of the domain knowledge of an application.


Only in the case of the MC-module the difference is about 5%.

the result for the isolated evaluation of the MC-module suggests that this is mainly due to errors caused by previous components.


The output document from constrained HMM contains MUC-standard NE.tags such as person, location and organization.

for a real information extraction system, the MUC-standard NE tag may not be enough and further detailed NE information might be necessary.


Further adding the morphological analysis capability that automatically analyzes unknown words (deriving additional morphological relationships and some semantic subsumption relationships) significantly improves that result (from 50.0% to 60.7%).

we found that adding the same semantic subsumption relationships to the commercial search engine, using its provided thesaurus capability degraded its results, and results were still degraded when we added only those facts that we knew would help find relevant documents.


Knowledge classifiers are part of almost any knowledge representation system.

the problem we face here is more difficult.


By default this concept is placed under policy.

in WordNet there is a hierarchy fiscal policy -IS-Aeconomic policy -IS-A -policy.


Higher interest rates are normally associated with weaker bond markets.

if interest rates go down, bonds go up, and your bond becomes more valuable.


Overgeneration will produce some name variants that have nothing to do with the company.

although overgeneration of variants routinely occurs, testing showed that such overgeneration has little adverse effect.


The prior work most closely related to this study is (Riloff, 1996), which, along with (Riloff, 1993), seeks automatic methods for filling slots in event templates.

the prior work differs from that presented here in several crucial respects; firstly, the prior work does not attempt to find entire events, after the fashion of MUC's highest-level scenario-template task.


Name lists provide an extremely efficient way of recognising names, as the only processing required is to match the name pattern in the list against the text and no expensive advanced processing such as full text parsing is required.

name lists are a naive method for recognising names.


We have also demonstrated that it is possible for the addition of corpus-derived lists to improve the performance of a NE recognition system based on gazetteers.

this is not guaranteed and it appears that adding too many names without any restriction may actually lead to poorer results, as happened when the LONG_TRAIN lists were applied.


HS also uses a frequency measure to provide an approximation of topic significance.

instead of counting frequency of stems or repetition of word sequences, this method counts frequency of a relatively easily identified grammatical element, heads of simplex noun phrases (SNPs).


Toward this end, we made several decisions about presentation of the data: 1.

threshold: So that no bias would be unintentionally introduced, we presented subjects with all terms output by each method, up to a specified cut-off poin-using lists of equal length for each method would have necessitated either omitting HSs and KWs or changing the definition of tts.


The graph in Figure 3 shows results for quality, not coverage.

figure 4, which shows the total number of terms rated at or below specified rankings, allows us to measure quality and coverage.


For example, the phrase 'Wall Street Journal' was included on the HS list only because it is specified as the document source.

four of the eight students assigned this term a high rating (1 or 2); this is puzzling because the document is about asbestos-related disease.


For example, the word exposure received an average rating of 2.2 when it appeared on the KW list, but a rating of only 2.75 on the HS list.

the more specific phrase racial quotas, which immediately followed quota on the HS list received a rating of 1.


One might imagine that a more carefully constructed lexicon could reduce the OOV rate for PERSONs while still staying within the 60,000 word limit.

even if a cleverly designed 60K lexicon succeeded in having the name coverage of the frequency-ordered 120K word lexicon (which contains roughly 40,000 more proper names than the 60K lexicon), it would reduce the PERSON OOV rate by only 4% absolute.


Once an example is in the training, IdentiFinder is able to extract it and use it in test.

when the test data is recognizer output, the rare names are less likely to appear in the test, either because they don't appear in the speech lexicon or they are poorly trained in the speech model and misrecognized.


Other GUI interface tools, such as a search field with command completion, can be envisioned that would provide direct access.

it is arguable that such an interface element belongs squarely to graphical user interfaces, but draws more on features of language.


In our case, the output of the dialog manager of a spoken dialog system provides the input to our sentence planner in the form of a single spoken dialog text plan for each of the turns.

(the dialog managers of most dialog systems today simply output completely formed utterances which are passed on to the TTS module.)


Perfect performance would have meant that there would be no significant difference.

the mean of BEST is 4.82 as compared with the mean of SPOT of 4.56, for a mean difference of 0.26 on a scale of 1 to 5.


Using hand-crafted evaluation metrics, they show that a genetic algorithm achieves good results in finding discourse trees.

they do not address clausecombining, and we do not use hand-crafted metrics.


The basic idea is that we view the labeling of new name ( l) as a transformation of the labeling of the old one ( l ).

we do not know l so we have to sum over all possible former labelings L .


In a similar way, we marked both "Dr." and "Sir" as honorifics in our test data.

the Wall Street Journal treats them very differently from "Mr." in that the former tend to be included even in the first mention of a name, while the latter is not.


Unfortunately, because "Mikio" does not appear elsewhere, the program is at a loss to decide which label to give it.

because Yotaro is assumed to be a first name, the program makes "Mikio Suzuki" coreferent with "Yotaro Suzuki" by labeling "Mikio" descriptor.


Naive Baseline The probabilistic model described in Section 2 explicitly takes adjective/adverb and verb co-occurrences into account.

one could derive meanings for polysemous adjective-noun combinations by solely concentrating on verb-noun relations, ignoring thus the adjective/adverb and verb dependencies.


More recently, (Gonzalo et al., 2000) also discusses potential usefulness of systematic polysemy for clustering word senses for IR.

extracting systematic relations from large sense inventories is a di cult task.


As a note, our lexicon is similar to CORELEX (Buitelaar, 1998) (or CORELEX-II presented in (Buitelaar, 2000)), in that both lexicons share the same motivation.

our lexicon di ers from CORELEX in that CORELEX looks at all senses of a w ord and groups words that have the same sense distribution pattern, whereas our lexicon groups \drop", \circle", \intersection", \dig", \crossing", \bull's eye" ARTIFACT-GROUP STRUCTURE, PEOPLE] \house", \convent", \market", \center" ARTIFACT-SUBSTANCE FABRIC, CHEMICAL COMPOUND] \acetate", \nylon", \acrylic", \polyester" COMMUNICATION-PERSON VOICE, SINGER] \soprano", \alto", \tenor", \baritone" WRITING, RELIGIOUS PERSON] \John", \Matthew", \Jonah", \Joshua", \Jeremiah" word senses that have the same systematic relation.


These two languages were chosen as the development set because they are represented by the most complete vocabularies and share the largest number of cognates.

as it turned out later, they are also the most closely related among the four Algonquian languages, according to all measures of phonetic similarity.


Most of the improvement comes from detecting entries that have matching glosses.


In particular, decision trees can be converted into implicational rules that an expert could inspect and can in principle be compiled back into finite-state machines (Sproat and Riley, 1996), although that would re-introduce the original efficiency problems.

finite-state transducers have the advantage of being invertible, which can be exploited e. g. for testing hand-crafted rule sets.


This points to a fundamental shortcoming of the usual two-step procedure, which we followed here: the goodness of an alignment performed in the first step should be determined by the impact it has on producing an optimal classifier that is induced in the second step.

there is no provision for feedback from the second step to the first step.


For example, Core and Schubert [8] point to counterexamples such as "have the engine take the oranges to Elmira, um, I mean, take them to Corning" where the antecedent of "them" is found in the EDITED words.

we believe that the assumption is so close to true that the number of errors introduced by this assumption is small compared to the total number of errors made by the system.


All words are assigned one of the two possible labels, EDITED or not.

in our evaluation we report the accuracy of only words other than punctuation and filled pauses.


For example, [13] uses a subsection of the ATIS corpus, takes as input the actual speech signal (and thus has access to silence duration but not to words), and uses as its evaluation metric the percentage of time the program identifies the start of the interregnum (see Section 2.2).

[9,10] use an internally developed corpus of sentences, work from a transcript enhanced with information from the speech signal (and thus use words), but do use a metric that seems to be similar to ours.


Important technical problems involving leftrecursive and unit productions are examined and overcome in (Stolcke, 1995).

these complications do not add any further machinery to the parsing algorithm per se beyond the grammar rules and the dot-moving conventions: in particular, there are no heuristic parsing principles or intermediate structures that are later destroyed.


Previous studies in unsupervised methods for parsing have concentrated on the use of inside-outside algorithm (Lari and Young, 1990;Carroll and Rooth, 1998).

there are several limitations of the inside-outside algorithm for unsupervised parsing, see (Marcken, 1995) for some experiments that draw out the mismatch between minimizing error rate and iteratively increasing the likelihood of the corpus.


Other approaches have tried to move away from phrase structural representations into dependency style parsing (Lafferty et al., 1992;Fong and Wu, 1996).

there are still inherent computational limitations due to the vast search space (see (Pietra et al., 1994) for discussion).


Yet we illustrated that induced semantics can help overcome some of these errors.

we have since observed that induced semantics can give rise to different kinds of problems.


Despite the semantic, orthographic, and syntactic components of the algorithm, there are still valid PPMVs, (X<Y), that may seem unrelated due to corpus choice or weak distributional properties.

x and Y may appear as members of other valid PPMVs such as (x<Z) and (Z<Y) containing variants (Z, in this case) which are either semantically or syntactically related to both of the other words.


We will incorporate the variable context length model into our system.°C onsidering more predictable bound In our experiments, we introduce new types of voting methods which stem from the theorems of SVMs -VC bound and Leave-One-Out bound.

chapelle and Vapnik introduce an alternative and more predictable bound for the risk and report their proposed bound is quite useful for selecting the kernel function and soft margin parameter (chapelle and Vapnik, 2000).


These approaches have produced interesting results, including applications involving real world dialogue systems.

reinforcement learning suffers from the fact that it is state based.


Many of these take a usercentric approach based on Wizard of Oz studies and iterative design (Bernsen et al., 1998).

there are still no precise guidelines about when to use specific techniques such as mixed-initiative.


A typical method used for NE extraction of Japanese texts is a cascade of morphological analysis, POS tagging and chunking.

there are some cases where segmentation granularity contradicts the results of morphological analysis and the building units of NEs, so that extraction of some NEs are inherently impossible in this setting.


Then, the shorter units tend to be hidden behind the longer unit words.

introducing the shorter unit words is more necessary to named entity extraction to generalize the model, because the shorter units are shared by many compound words.


Our work presents a novel knowledge-lean algorithm that uses multiple-sequence alignment (MSA) to learn to generate sentence-level paraphrases essentially from unannotated corpus data alone.

to previous work using MSA for generation (Barzilay and Lee, several versions of their component sentences.


Previous approaches to paraphrase acquisition focused on certain rigid types of paraphrases, for instance, limiting the number of arguments.

our method is not limited to a set of a priori-specified paraphrase types.


For example, (Clarke et al., 2003) and (Lin et al., 2003) employ techniques for utilizing both unstruc-tured text and structured databases for question answering.

the approaches taken by both these systems differ from ours in that they enforce an order between the two strategies by attempting to locate answers in structured databases first for select question types and falling back to unstructured text when the former fails, while we explore both options in parallel and combine the results from multiple answering agents.


Such knowledge would include both the spelling and pronunciation of a new word, as well as an understanding of its usage in the language (e.g., a semantic category).

this is a difficult task to carry out effectively, challenging both with regard to the automatic acquisition of the sound-to-letter mapping from typically telephone-quality speech, and the system level aspect of integrating the usually off-line activities of system upgrade while seamlessly continuing the conversation with the user.


Deletions occur when part of the spelled portion is mistakenly identified as part of the unknown word or insertions arise when the end of a spoken word is confused for a spelled letter.

the multi-stage system produces a marked improvement if we compare it with the single-stage letter recognizer as a baseline.


Our final selection process is based only on the proposed spellings obtained from the pronounced word, after feeding information from the spelled part into the second stage.

performance may improve if we apply a strict constraint during the search, explicitly allowing only paths where the spoken and spelled part of the waveforms agree on the name spelling.


These cases are of central interest in language and speech processing.

automata with other kinds of weights can also be defined.


We have found no simple necessary and sufficient condition on (K, ⊗) that guarantees a globally consistent set of choices to exist.

we have given a useful nec-essary condition (greedy factorization), and we now give a useful sufficient condition.


if we limit the size of the swapped regions to a constant I and their distance to a constant P , we can reduce the number of swaps performed to a linear function of the input length.


The complexity of calculating the alignment probability globally (that is, over the entire alignment) is ¢ ¡£ § .

since there is a constant upper bound 3 on the size of local contexts, needs to be performed only once for the initial gloss, therafter, recalculation of only those probabilities affected by each change ( ) suffices.


For the Arabic test data from the same evaluation, we obtained a similar shape (although with a roughly level plateau).

the 'bumpiness' of the surface raises the question as to which of these differences are statistically We are aware of several ways to determine the statistical significance of BLEU score differences.


For example, both relations ¡ apartment 1; woman 1; No¢ and ¡ hand 1; woman 1; Yes¢ are mapped into the more general type ¡ entity 1; entity 1; Yes/No¢ .

the first example is negative (a POSSESSION relation), while the second one is a positive example.


For the examples described above, the procedure eliminates the ambiguity through specialization of the semantic classes into two new ones: whole -causal agent, and respectively part -causal agent.

if the training corpus contained the examples ¡ leg 2; insect 1; Yes¢ and ¡ world 7; insect 1; No¢ , the procedure specializes them in the ambiguous example ¡ part 7; organism 1; Yes/No¢ and the ambiguity still remains.


Word sense disambiguation could, therefore, also independently be performed using the semantic coherence scoring described herein as an additional application of our approach.

that has not been investigated thoroughly yet.


128 words (or 61% of missing words) were not actually missing, but rather not linked into the set of clusters evaluated by a particular annotator.


Schuetze (1998) proposed a method for dividing occurrences of a word into classes, each of which consists of contextually similar occurrences.

it does not produce definitions of senses such as sets of synonyms and sets of translation equivalents.


Generally speaking, the problem becomes more difficult in translingual distributional word clustering, since the sparseness of data in two languages is multiplied.

the sense-vs.-clue correlation matrix calculation method overcomes this difficulty; it calculates the correlations between senses and clues iteratively to smooth out the sparse data.


4) Cluster senses by using a hierarchical agglomerative clustering method, e.g., the group-average method.

this naive method is not effective because some senses usually have duplicated definitions in step 1) despite the fact that the sense-vs.-clue correlation matrix calculation algorithm presupposes a set of senses without duplicated definitions.


To situate our results, the FOMs used by (Caraballo and Charniak, 1998) require 10K edges to parse 96% of these sentences, while BF requires only 6K edges.

the more complex, tuned FOM in  is able to parse all of these sentences using around 2K edges, while BF requires 7K edges.


We first observe that the complete set of word alignments generated by the ATTM (ATTM-C) is relatively poor.

when we consider only those word alignments generated by actual alignment templates (ATTM-A) (and discard the alignments generated by the dummy templates introduced as described in Section 3.1), we obtain very high alignment precision.


These components can be refined and improved by changing the corresponding transducers without requiring changes to the overall search procedure.

some of the modeling assumptions are extremely strong.


The Document Understanding Conference (DUC 2002) run by the National Institute of Standards and Technology (NIST) sets out to address this problem by providing annual large scale common evaluations in text summarization.

these evaluations involve human judges and hence are subject to variability (Rath et al.


It consistently correlated highly with human assessments and had high recall and precision in significance test with manual evaluation results.

the weighted average of variable length n-gram matches derived from IBM BLEU did not always give good correlation and high recall and precision.


For their purposes, they mainly need to ensure the correctness of consensus among different translations, so that different constituent orderings in input sentences do not pose a serious prob-  lem.

we want to ensure the correctness of all paths represented by the FSAs, and direct application of MSA in the presence of different constituent orderings can be problematic.


From the curves, one can see that as the order increases, classification accuracy increases and testing entropy decreases, presumably because the longer context better captures the regularities of the text.

at some point accu-  Figure 2: The entropy of different n-gram models racy begins to decrease and entropy begins to increase as the sparse data problems begin to set in.


Even if all f -structures are transferred from the same linguistically rich source, and all generated strings are grammatical, a reduction in error rate of around 50% relative to the upper bound can be achieved by stochastic selection.

a comparison between transfer runs with and without perfect disambiguation of the original string shows a decrease of about 5% in F-score, and of only .1 points for summarization quality when transferring from packed parses instead of from the manually selected parse.


In our case, combining the counts in this way yielded a half a point, perhaps because of the in-domain tuning of the smoothing parameters.

when we optimize α empirically on the held-out corpus, we can get nearly a full point improvement.


Furthermore, they are trained to minimize some function related to labeling error, leading to smaller error in practice if enough training data are available.

generative models are trained to maximize the joint probability of the training data, which is not as closely tied to the accuracy metrics of interest if the actual data was not generated by the model, as is always the case in practice.


In contrast, generative models are trained to maximize the joint probability of the training data, which is not as closely tied to the accuracy metrics of interest if the actual data was not generated by the model, as is always the case in practice.

since sequential classifiers are trained to make the best local decision, unlike generative models they cannot trade off decisions at different positions against each other.


The ap-proximated diagonal term H f for feature f has the form If this approximation is semidefinite, which is trivial to check, its inverse is an excellent preconditioner for early iterations of CG training.

when the model is close to the maximum, the approximation becomes unstable, which is not surprising since it is based on feature independence assumptions that become invalid as the weights of interaction features move away from zero.


Mixed CG training converges slightly more slowly than preconditioned CG.

cG without preconditioner converges much more slowly than both preconditioned cG and mixed cG training.


On the other hand, CG without preconditioner converges much more slowly than both preconditioned CG and mixed CG training.


On the application side, (log-)linear parsing models have the potential to supplant the currently dominant lexicalized PCFG models for parsing by allowing much richer feature sets and simpler smoothing, while avoiding the label bias problem that may have hindered earlier classifier-based parsers (Ratnaparkhi, 1997).

work in that direction has so far addressed only parse reranking (Collins and Duffy, 2002;Riezler et al., 2002).


In order to use more information, we might imagine using values of ec directly, rather than thresholding.

this quickly leads to data sparsity problems.


Various linguistics studies have also shown how intertwined syntax and discourse are (Maynard, 1998).

to our knowledge, this is the first paper that empirically shows that the connection between syntax and discourse can be computationally exploited at high levels of accuracy on open domain, newspaper text.


Little improvement comes from using manually built syntactic parse trees instead of automatically derived trees.

experiments show that there is much to be gained if better discourse segmentation algorithms are found; 83% accuracy on this task is not sufficient for building highly accurate discourse trees.


Current state-of-the-art statistical parsers (Collins, 1999;Charniak, 2000) are trained on large annotated corpora such as the Penn Treebank (Marcus et al., 1993).

the production of such corpora is expensive and labor-intensive.


One such criterion is the accuracy of the labeled examples, which may be estimated by the teacher parser's confidence in its labels.

the examples that the teacher correctly labeled may not be those that the student needs.


For the same parser performance level, it selects the fewest number of sentences for a human to check and requires the human to make the least number of corrections.

as we have seen in the earlier experiment, very few sentences in the unlabeled pool satisfy its stringent criteria, so it ran out of data before the parser was trained to convergence.


Alternatively, the number of documents that contain the pair can also be used.

the nature of the language tests in this work make it impractical to be applied.


In a sequence will to fight, that trend indicates that will should be a noun rather than a modal verb.

that effect is completely lost in a CMM like (a): P(t will |will, star t ) prefers the modal tagging, and P(TO|to, t will ) is roughly 1 regardless of t will .


to-left model, fight will receive its more common noun tagging by symmetric reasoning.

the bidirectional model (c) discussed in the next section makes both directions available for conditioning at all locations, using replicated models of P(t 0 |t −1 , t +1 , w 0 ), and will be able to get this example correct.


Since this model has roughly twice as many tag-tag features, the fact that it outperforms the unidirectional models is not by itself compelling evidence for using bidirectional networks.

it also outperforms model L+L 2 which adds the t 0 , t −2 secondprevious word features instead of next word features, which gives only 96.05% (and R+R 2 gives 95.25%).


When an information seeker can readily think up a suitable term or linguistic expression to represent the information need, direct searching of text by user-generated terms is faster and more effective than browsing.

when users do not know (or can't remember) the exact expression used in relevant documents, they necessarily struggle to find relevant information in full-text search systems.


The extracts generated are in size shorter than the original texts.

the number of sentences that E states emit cannot be predetermined.


In the LNK task, incorrectly flagging two stories as being on the same event is considered a false alarm.

in the NED task, incorrectly flagging two stories as being on the same event will cause a true first story to be missed.


A LNK system should minimize false alarms by identifying only linked stories, which results in high precision for LNK.

a NED system will minimize false alarms by identifying all stories that are linked, which translates to high recall for LNK.


A corpus-based approach is able to quickly build a machine translation system for a new domain if a bilingual corpus of that domain is available.

if only a small-sized corpus is available, a low translation quality is obtained.


These methods evaluate the quality of the translation by measuring the similarity between machine translation results and translations done by humans (called references).

the accuracy increases when multiple references are applied because one source sentence can be translated into multiple target expressions.


However, no nodes contain only the links 'get', and 'me' in the lower sentence.

focusing on the upper phrase "get me a taxi," it contains four word links that correspond to the lower phrase "get a taxi for me", and they have the same syntactic category.


The hope is that the same techniques will work for extracting prefixes.

even that will not handle the complex combi-nations of infixes that are possible in agglutinative languages like Turkish or polysynthetic languages like Inuktitut.


In naturally occurring text, the more frequent sense for the two-sense distinction is reported to occur 92% of the time on average; this result has been found both on the CACM collection and on the WordNet SEMCOR sensetagged corpus (Sanderson & van Rijsbergen, 1999).

the challenge for WSD programs is to work on the harder cases, and the artificially constructed SENSE-VAL1 corpus has more evenly distributed senses (Gaustad, 2001).


In a manually built corpus, a coreference chain can include pronouns and common nouns that refer to the person.

these forms could not be automatically identified, so coreference chains in our corpus only include noun phrases that contain at least one word from the name.


In this paper we describe CarmelTC , a novel automatic essay grading approach using a hybrid text classification technique for analyzing essay answers to qualitative physics questions inside the Why2 tutorial dialogue system (VanLehn et al., 2002).

to many previous approaches to automated essay grading (Burstein et al., 1998;Larkey, 1998), our goal is not to assign a letter grade to student essays.


In general, the performance of existing speech recognition systems, whose designs are predicated on relatively noise-free conditions, degrades rapidly in the presence of a high level of adverse conditions.

a recognizer can provide good performance even in very noisy background conditions if the exact testing condition is used to provide the training material from which the reference patterns of the vocabulary are obtained, which is practically not always the case.


Thus, a system could map both verbs wipe and remove onto the same action scheme.

the apparently equivalent transformations from (1a) to (1b) and from (2a) to (2b) show otherwise.


One may say that it is not semantically ambiguous.

simple algorithms such as maximal matching [6,9] and longest matching [6] may not be able to discriminate this kind of ambiguity.


Within this framework, any type of feature can be used, enabling the system designer to experiment with interesting feature types, rather than worry about specific feature interactions.

in a rule based system, the system designer would have to consider how, for instance, a WordNet (Miller, 1995) derived information for a particular example interacts with a part-of-speech-based information and chunking information.


Since the documents were selected by subject, one may argue that the task of clustering entities will be much easier if the entities are clearly from different genres.

if this is true, then it may account for about 85% of the entities in the person-x corpus that occur only in one domain subject.


The form of the salience metric, and the choice of features that factor into it, is governed by our knowledge about the way speech and gesture work.

the penalty function also requires parameters that weigh the importance of each factor.


This is an uncommon phenomenon, and as such, was penalized highly.

anecdotally it appears that the presence of a disfluency makes this phenomenon more likely.


Their approach of seeking to maximize this probability is similar to the saliencemaximizing approach that we have described.

instead of using a parametric salience function, they learn a set of conditional probability distributions directly from the data.


However, scalability of such systems is a bottleneck due to the heavy cost of authoring and maintenance of rule sets and inevitable brittleness due to lack of coverage in the rule sets.

data-driven approaches are robust and the procedure for model building is usually simple.


Data-driven approaches are robust and provide a simple process of developing applications given the data from the application domain.

the reliance on domain-specific data is also one of the significant bottlenecks of data-driven approaches.


For a system at ability level 0, the odds increase by another factor of 2.72 to 7.39, giving a probability of .88.

a system with an ability of -3, would have the even odds decrease by a factor of 2.72 to .369, yielding P sq = .27.


Second, for obvious reasons the raw-score estimates based on the Easy sets are considerably higher than those based on the Hard sets.

table 2 also shows that the standard deviations of the number correct estimates obtained for the Easy sets exceed those of the Hard sets as well (sometimes by over 100%).


There are many multi-word expressions whose hypernyms are their suffixes, and if some expressions share a common suffix, it is likely to be their hypernym.

if a hypernym candidate appears in a position other than as a suffix of a hyponym candidate, the hypernym candidate is likely to be an erroneous one.


Sentences are generally demarcated by a major fall (or rise) in f0, lengthening of the final syllable, and following pauses.

the usefulness of prosodic information in sentenceinternal parsing is less clear.


Simple statistical tests show that there is in fact a significant correlation between the location of opening and closing phrase boundaries and all of the prosodic pseudo-punctuation symbols described above, so there is no doubt that these do convey information about syntactic structure.

adding the prosodic pseudo-punctuation symbols uniformly decreased parsing accuracy relative to input with no prosodic information.


(2003) consider selection of individual AL methods at run-time.

their AL methods are only ever based on single model approaches.


For example, the categories of the Model 3 Collins parser distinguish between heads, arguments, and adjuncts and they mark some longdistance dependency paths; these distinctions can guide application-specific postprocessors in extracting important semantic relations.

state-of-the-art parsing systems based on deep grammars mark explicitly and in much more detail a wider variety of syntactic and semantic dependencies and should therefore provide even better support for meaning-sensitive applications.


Moreover, a surprising variety of problems are attackable with FSTs, from partof-speech tagging to letter-to-sound conversion to name transliteration.

language problems like machine translation break this mold, because they involve massive reordering of symbols, and because the transformation processes seem sensitive to hierarchical tree structure.


Section 1 informally described the root-to-frontier transducer class R. We saw that R allows, by use of states, finite lookahead and arbitrary rearrangement of nonsibling input subtrees removed by a finite distance.

it is often easier to write rules that explicitly represent such lookahead and movement, relieving the burden on the user to produce the requisite intermediary rules and states.


In arbitrary document collections, such patterns might be too variable to be easily detected by statistical means.

research has shown that texts from the same domain tend to exhibit high similarity (Wray, 2002).


We conjecture that this difference in performance stems from the ability of content models to capture global document structure.

the other two algorithms are local, taking into account only the relationships between adjacent word pairs and adjacent sentence pairs, respectively.


Keller and Lapata's (2003) results suggest that webbased frequencies can be a viable alternative to bigram frequencies obtained from smaller corpora or recreated using smoothing.

they do not demonstrate that realistic NLP tasks can benefit from web counts.


Phone recognition is known to be less accurate than word recognition.

the second method can only generate phone strings that are substrings of the pronunciations of in-vocabulary word strings.


We have catalogued a variety of such relationships, and note here that we believe it could prove useful to address semantic interdependencies among SCUS in future work that would involve adding a new annotation layer.

4 in our approach, SCUs are treated as independent annotation values, which has the advantage of affording a rigorous analysis of interannotator reliability (see following section).


This makes sense in light of the fact that a score is dominated by the higher weight SCUS that appear in a summary.

we wanted to study more precisely at what point scores become independent of the choice of models that populate the pyramid.


First, an SCU is a set of contributors that are largely similar in meaning, thus SCUs differ from each other in both meaning and weight (number of contributors).

factoids are semi-formal expressions in a FOPL-style semantics, which are compositionally interpreted.


The model in (6) is simplistic in that the relationships between the features across the clauses are not captured directly.

if two values of these features for the main and subordinate clauses co-occur frequently with a particular marker, then the conditional probability of these features on that marker will approximate the right biases.


The majority of the main clauses in our data are sentence intitial (80.8%).

there are differences among individual markers.


Knowing the linear precedence of the two clauses is highly predictive of their type: 80.8% of the main clauses are sentence initial.

this type of positional information is typically not known when fragments are synthesised into a meaningful sentence.


Given that different Machine Translation (MT) evaluation metrics are useful for capturing different aspects of translation quality, it becomes desirable to create MT systems tuned with respect to each individual criterion.

the maximum likelihood techniques that underlie the decision processes of most current MT systems do not take into account these application specific goals.


The two hypothesis translations are very similar at the word level and therefore the BLEU score, PER and the WER are identical.

we observe that the sentences differ substantially in their syntactic structure (as seen from Parse-Trees in Figure 3), and to a lesser extent in their word-to-word alignments (Figure 1) to the source sentence.


In (Och and Weber, 1998;Och et al., 1999), a two-level alignment model was employed to utilize shallow phrase structures: alignment between templates was used to handle phrase reordering, and word alignments within a template were used to handle phrase to phrase translation.

phrase level alignment cannot handle long distance reordering effectively.


One is the PRank algorithm, a variant of the perceptron algorithm, that uses multiple biases to represent the boundaries between every two consecutive ranks (Crammer and Singer, 2001;Harrington, 2003).

as we will show in section 3.7, the PRank algorithm does not work on the reranking tasks due to the introduction of global ranks.


In addition, we only want to maintain the order of two candidates if their ranks are far away from each other.

we do not care the order of two translations whose ranks are very close, e.g.


As a result, we cannot use the PRank algorithm in the reranking task, since there are no global ranks or boundaries for all the samples.

the approach of using pairwise samples does work.


It achieves a BLEU score of 31.7% on the Baseline, 32.8% on the Best Feature, but only 32.6% on the Top Twenty features.

it is within the range of 95% confidence.


Much of this research has used databases of speech read by actors or native speakers as training data (often with semantically neutral content) (Oudeyer, 2002;Polzin and Waibel, 1998;Liscombe et al., 2003).

such prototypical emotional speech does not necessarily reflect natural speech (Batliner et al., 2003), such as found in tutoring dialogues.


Although much past work predicts only two classes (e.g., negative/non-negative) (Batliner et al., 2003;Ang et al., 2002;Lee et al., 2001), our experiments produced the best predictions using our three-way distinction.

to (Lee et al., 2001), our classifications are contextrelative (relative to other turns in the dialogue), and taskrelative (relative to tutoring), because like (Ang et al., 2002), we are interested in detecting emotional changes across our dialogues.


Comparing the results of these combined (speech+text) feature sets with the speech versus text results in Table 1, we find that for autotext+speech-ident and all +ident feature sets, the combined feature set slightly decreases predictive accuracy when compared to the corresponding text-only feature set.

there is no significant difference between the best results in each table (all-text+speech+ident vs. alltext+ident).


Comparing these results with the results in Tables 1 and 2, we find that while overall the performance of contextual non-combined feature sets shows a small performance increase over most non-contextual combined or non-combined feature sets, there is again a slight decrease in performance across the best results in each table.

there is no significant difference between these best results (alltext+glob-ident vs. all-text+speech+ident vs. alltext+ident).


Ideally, by training acoustic models on target non-native speech, one would capture its specific characteristics just as training on native speech does.

collecting amounts of non-native speech that are large enough to fully train speaker-independent models is a hard and often impractical task.


Yet, this is acceptable given the small amount of training data for the language model and the conversational nature of the speech.

performance degrades significantly for non-native speakers, with a word error rate of 52.0%.


For example, when referring to bus stops by street intersections, all native speakers in our training set simply used "A and B", hence the word "intersection" was not in the language model.

many non-native speakers used the full expression "the intersection of A and B".


In fact, this is not only true for non-native speakers and lexical entrainment is often described as a negotiation process between the speakers (Clark and Wilkes-Gibbs, 1986).

while it is possible for limited-domain system designers to establish a set of words and constructions that are widely used among native speakers, the variable nature of the expressions mastered by non-native speakers make adaptation a desirable feature of the system.


Partial Path -For the argument identification task, path is the most salient feature.

it is also the most data sparse feature.


On performing the search, we found that the overall performance improvement was not much different than that obtained by resolving overlaps as mentioned earlier.

we found that there was an improvement in the CORE ARGUMENT accuracy on the combined task of identifying and assigning semantic arguments, given hand-corrected parses, whereas the accuracy of the ADJUNCTIVE ARGUMENTS slightly deteriorated.


Although, there was an increase in F 1 score when the language model probabilities were jointly estimated over all the predicates, this improvement is not statistically significant.

estimating the same using specific predicate lemmas, showed a significant improvement in accuracy.


Speech form is familiar to humans, and can convey information effectively (Nadamoto et al., 2001;Hayashi et al., 1999).

little electronic information is provided in speech form so far.


However, little electronic information is provided in speech form so far.

there is a lot of information in text form, and it can be transformed into speech by a speech synthesis.


There are several studies that compare two corpora which have different styles, for example, written and spoken corpora or British and American English corpora, and try to find expressions unique to either of the styles (Kilgarriff, 2001).

those studies did not deal with paraphrases.


This paper has focused only on paraphrasing predicates.

there are other kinds of paraphrasing which are necessary in order to paraphrase written language text into spoken language.


On the one hand, we have three different variants of the single-word based model IBM4.

we have two phrase-based systems, namely the alignment templates and the one described in this work.


This method yielded word classes that offered more robust count approximations for their member words.

both methods yielded similar results when embedded in the larger system, and so we will report on the results of using Good-Turing so as to remain more directly comparable to Dagan et al.


The MaxEnt model selected the temerity as the antecedent of its (salience value: 0.30), preferring it to the correct antecedent the endowment (salience value: 0.10).

altaVista found no occurrences of temerity's money or its variants on the web, and thus the unnormalized and normalized counts were 0.


However, AltaVista found no occurrences of temerity's money or its variants on the web, and thus the unnormalized and normalized counts were 0.

endowment's money and its variants had unnormalized and normalized statistics of 1583 and 1.47 × 10 −3 respectively.


In the cases in which statistics reinforced a wrong answer, no (reasonable) manipulation of statistical features or filters can rescue the prediction.

for the cases in which statistics could help, their successful use will depend on the existence of a formula that can capture these cases without changing the predictions for examples that the model currently classifies correctly.


Since pronouns carry little semantics of their own, resolving them depends almost entirely on context.

even though context can be helpful for resolving definite NPs, context can be trumped by the semantics of the nouns themselves.


For example, sources in a language that is translated to English will consistently use the same terminology, resulting in greater similarity between linked documents with the same native language.

sources from radio broadcasts may be transcribed much less consistently than text sources due to recognition errors, so that the expected similarity of a radio broadcast and a text source is less than that of two text sources.


We refer to the statistics characterizing story pairs with the same source types as source-pair specific information.

to the source-specific thresholds used by CMU, we normalize the similarity measures based on the sourcepair specific information, simultaneously with combining different similarity measures.


SVM-based systems, such as that described in (Joachims, 1998), are typically among the best performers for the categorization task.

attempts to directly apply SVMs to TDT tasks such as tracking and link detection have not been successful; this has been attributed in part to the lack of enough data for training the SVM 1 .